10,710 research outputs found
Tolerating Correlated Failures in Massively Parallel Stream Processing Engines
Fault-tolerance techniques for stream processing engines can be categorized
into passive and active approaches. A typical passive approach periodically
checkpoints a processing task's runtime states and can recover a failed task by
restoring its runtime state using its latest checkpoint. On the other hand, an
active approach usually employs backup nodes to run replicated tasks. Upon
failure, the active replica can take over the processing of the failed task
with minimal latency. However, both approaches have their own inadequacies in
Massively Parallel Stream Processing Engines (MPSPE). The passive approach
incurs a long recovery latency especially when a number of correlated nodes
fail simultaneously, while the active approach requires extra replication
resources. In this paper, we propose a new fault-tolerance framework, which is
Passive and Partially Active (PPA). In a PPA scheme, the passive approach is
applied to all tasks while only a selected set of tasks will be actively
replicated. The number of actively replicated tasks depends on the available
resources. If tasks without active replicas fail, tentative outputs will be
generated before the completion of the recovery process. We also propose
effective and efficient algorithms to optimize a partially active replication
plan to maximize the quality of tentative outputs. We implemented PPA on top of
Storm, an open-source MPSPE and conducted extensive experiments using both real
and synthetic datasets to verify the effectiveness of our approach
KV-match: A Subsequence Matching Approach Supporting Normalization and Time Warping [Extended Version]
The volume of time series data has exploded due to the popularity of new
applications, such as data center management and IoT. Subsequence matching is a
fundamental task in mining time series data. All index-based approaches only
consider raw subsequence matching (RSM) and do not support subsequence
normalization. UCR Suite can deal with normalized subsequence match problem
(NSM), but it needs to scan full time series. In this paper, we propose a novel
problem, named constrained normalized subsequence matching problem (cNSM),
which adds some constraints to NSM problem. The cNSM problem provides a knob to
flexibly control the degree of offset shifting and amplitude scaling, which
enables users to build the index to process the query. We propose a new index
structure, KV-index, and the matching algorithm, KV-match. With a single index,
our approach can support both RSM and cNSM problems under either ED or DTW
distance. KV-index is a key-value structure, which can be easily implemented on
local files or HBase tables. To support the query of arbitrary lengths, we
extend KV-match to KV-match, which utilizes multiple varied-length
indexes to process the query. We conduct extensive experiments on synthetic and
real-world datasets. The results verify the effectiveness and efficiency of our
approach.Comment: 13 page
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